Evading Data Contamination Detection for Language Models is (too) Easy

Jasper Dekoninck, Mark Niklas Mueller, Maximilian Baader, Marc Fischer, Martin Vechev

Keywords: large language models, model evaluation, malicious actors

TL;DR: We analyze current contamination detection methods and find a significant vulnerability in their assumptions that can be easily exploited by malicious actors.

Abstract: The benchmark performance of large language models (LLMs) has a high impact on their popularity and is thus of great importance to many model providers. However, the reliability of such benchmark scores as a measure of model quality gets compromised if the model is contaminated with benchmark data. While recent contamination detection methods try to address this issue, they overlook the possibility of deliberate contamination by malicious model providers aiming to evade detection. We propose a categorization of model providers based on their (de)contamination practices and argue that malicious contamination is of crucial importance as it casts doubt on the reliability of public benchmarks. To study this issue more rigorously, we analyze current contamination detection methods based on their assumptions. This analysis reveals a significant vulnerability in existing approaches: they do not account for rephrased benchmark data used during training by malicious actors. We demonstrate how exploiting this gap can result in significantly inflated benchmark scores while completely evading current detection methods.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 9617